如何在matplotlib中更改底部x轴顶部某些x轴记号标签的位置？

这是我当前的脚本：

#!/usr/bin/env python3

import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

"""
Setup for a typical explanatory-style illustration style graph.
"""

h = 2
x = np.linspace(-np.pi, np.pi, 100)
y = 2 * np.sin(x)
rc = {
    # Tick in the middle of the axis line.
    'xtick.direction' : 'inout',
    'ytick.direction' : 'inout',

    # Bold is easier to read when we have few ticks.
    'font.weight': 'bold',
    'xtick.labelbottom': False,
    'xtick.labeltop': True,
}
with plt.rc_context(rc):
    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_title(
        '2 sin(x), not $\\sqrt{2\\pi}$',
        # TODO make LaTeX part bold?
        # https://stackoverflow.com/questions/14324477/bold-font-weight-for-latex-axes-label-in-matplotlib
        fontweight='bold',
        # Too close otherwise.
        # https://stackoverflow.com/questions/16419670/increase-distance-between-title-and-plot-in-matplolib/56738085
        pad=20
    )

    # Custom visible plot area.
    # ax.set_xlim(-3, 3)
    ax.set_ylim(-2.5, 2.5)

    # Axes
    # Axes on center:
    # https://stackoverflow.com/questions/31556446/how-to-draw-axis-in-the-middle-of-the-figure
    ax.spines['left'].set_position('zero')
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_position('zero')
    ax.spines['top'].set_visible(False)
    # Axes with arrow:
    # https://stackoverflow.com/questions/33737736/matplotlib-axis-arrow-tip
    ax.plot(1, 0, ls="", marker=">", ms=10, color="k",
            transform=ax.get_yaxis_transform(), clip_on=False)
    ax.plot(0, 1, ls="", marker="^", ms=10, color="k",
            transform=ax.get_xaxis_transform(), clip_on=False)

    # Ticks
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    # Make ticks a bit longer.
    ax.tick_params(width=1, length=10)
    # Select tick positions
    # https://stackoverflow.com/questions/12608788/changing-the-tick-frequency-on-x-or-y-axis-in-matplotlib
    xticks = np.arange(math.ceil(min(x)),     math.floor(max(x)) + 1, 1)
    yticks = np.arange(math.ceil(min(y)) - 1, math.floor(max(y)) + 2, 1)
    # Remove 0.
    xticks = np.setdiff1d(xticks, [0])
    yticks = np.setdiff1d(yticks, [0])
    ax.xaxis.set_ticks(xticks)
    ax.yaxis.set_ticks(yticks)
    # Another approach. But because I want to be able to remove the 0,
    # anyways, I just explicitly give all ticks instead.
    # ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
    # ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))

    # Annotations.
    ax.plot([0, np.pi/2], [h, h], '--r')
    ax.plot([np.pi/2, np.pi/2], [h, 0], '--r')
    ax.plot(np.pi/2, h, marker='o', linewidth=2, markersize=10,
        markerfacecolor='w', markeredgewidth=1.5, markeredgecolor='black')

plt.savefig(
    'main.png',
    format='png',
    bbox_inches='tight'
)
plt.clf()

这是输出：

这就是我想要的（用GIMP进行黑客攻击），注意负刻度标签现在在轴的另一侧

我试着加上：

    for tick in ax.xaxis.get_majorticklabels():
        tick.set_verticalalignment("bottom")

如回答：中所示，但这并没有将记号标签向上移动足够多，而是使标签显示在轴的顶部

---

在matplotlib 3.2.2上测试。

以下代码将根据刻度的x值是负值还是正值来调整刻度的垂直对齐。但是，这还不够，因为标签实际上固定在刻度线的底部。因此，我将稍微调整它们的y位置，但您必须使用值来获得所需的输出

# adjust the xticks so that they are on top when x<0 and on the bottom when x≥0
ax.spines['top'].set_visible(True)
ax.spines['top'].set_position('zero')
ax.spines['bottom'].set_visible(True)
ax.spines['bottom'].set_position('zero')
ax.xaxis.set_tick_params(which='both', top=True, labeltop=True,
                             bottom=True, labelbottom=True)
fig.canvas.draw()
for tick in ax.xaxis.get_major_ticks():
    print(tick.get_loc())
    if tick.get_loc()<0:
        tick.tick1line.set_visible(False)
        tick.label1.set_visible(False)
    else:
        tick.tick2line.set_visible(False)
        tick.label2.set_visible(False)

---

<代码> >调整XTICK，以便席上注意到，当XI稍后注意到，这个解决方案不能很好地与数据大小进行缩放时，例如，如果我设置<代码> H＝1000 < /Cord>，则数字开始触摸滴答声。如果我删除添加的

for

循环，则它们不会删除。我猜可能有一个更合适的转换应用于magic

0.15

，这样一个数字可以用于任何数据大小。我找到了一个更好的方法来操作标签。我修改了我的回答啊，鬼鬼祟祟的，把两个轴都设置为可见，太棒了！

import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

"""
Setup for a typical explanatory-style illustration style graph.
"""

h = 10
x = np.linspace(-np.pi, np.pi, 100)
y = h * np.sin(x)
rc = {
    # Tick in the middle of the axis line.
    'xtick.direction' : 'inout',
    'ytick.direction' : 'inout',

    # Bold is easier to read when we have few ticks.
    'font.weight': 'bold',
    'xtick.labelbottom': False,
    'xtick.labeltop': True,
}
with plt.rc_context(rc):
    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_title(
        '2 sin(x), not $\\sqrt{2\\pi}$',
        # TODO make LaTeX part bold?
        # https://stackoverflow.com/questions/14324477/bold-font-weight-for-latex-axes-label-in-matplotlib
        fontweight='bold',
        # Too close otherwise.
        # https://stackoverflow.com/questions/16419670/increase-distance-between-title-and-plot-in-matplolib/56738085
        pad=20
    )

    # Custom visible plot area.
    # ax.set_xlim(-3, 3)
    ax.set_ylim(-2.5, 2.5)

    # Axes
    # Axes on center:
    # https://stackoverflow.com/questions/31556446/how-to-draw-axis-in-the-middle-of-the-figure
    ax.spines['left'].set_position('zero')
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_position('zero')
    ax.spines['top'].set_visible(False)
    # Axes with arrow:
    # https://stackoverflow.com/questions/33737736/matplotlib-axis-arrow-tip
    ax.plot(1, 0, ls="", marker=">", ms=10, color="k",
            transform=ax.get_yaxis_transform(), clip_on=False)
    ax.plot(0, 1, ls="", marker="^", ms=10, color="k",
            transform=ax.get_xaxis_transform(), clip_on=False)

    # Ticks
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    # Make ticks a bit longer.
    ax.tick_params(width=1, length=10)
    # Select tick positions
    # https://stackoverflow.com/questions/12608788/changing-the-tick-frequency-on-x-or-y-axis-in-matplotlib
    xticks = np.arange(math.ceil(min(x)),     math.floor(max(x)) + 1, 1)
    yticks = np.arange(math.ceil(min(y)) - 1, math.floor(max(y)) + 2, 1)
    # Remove 0.
    xticks = np.setdiff1d(xticks, [0])
    yticks = np.setdiff1d(yticks, [0])
    ax.xaxis.set_ticks(xticks)
    ax.yaxis.set_ticks(yticks)
    # Another approach. But because I want to be able to remove the 0,
    # anyways, I just explicitly give all ticks instead.
    # ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
    # ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
    
    for g,t in zip(ax.get_xticks(),ax.get_xticklabels()):
        if g<0:
            t.set_va('bottom')
        else:
            t.set_va('top')
        t.set_transform(ax.transData)
        t.set_position((g,0.15*-(g/abs(g))))

    # Annotations.
    ax.plot([0, np.pi/2], [h, h], '--r')
    ax.plot([np.pi/2, np.pi/2], [h, 0], '--r')
    ax.plot(np.pi/2, h, marker='o', linewidth=2, markersize=10,
        markerfacecolor='w', markeredgewidth=1.5, markeredgecolor='black')
    
    
    # adjust the xticks so that they are on top when x<0 and on the bottom when x≥0
    ax.spines['top'].set_visible(True)
    ax.spines['top'].set_position('zero')
    ax.spines['bottom'].set_visible(True)
    ax.spines['bottom'].set_position('zero')
    ax.xaxis.set_tick_params(which='both', top=True, labeltop=True,
                                 bottom=True, labelbottom=True)
    fig.canvas.draw()
    for tick in ax.xaxis.get_major_ticks():
        print(tick.get_loc())
        if tick.get_loc()<0:
            tick.tick1line.set_visible(False)
            tick.label1.set_visible(False)
        else:
            tick.tick2line.set_visible(False)
            tick.label2.set_visible(False)